Triple
T10803624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ornstein–Uhlenbeck process |
E254905
|
entity |
| Predicate | hasStationaryVariance |
P27168
|
FINISHED |
| Object | σ^2 / (2θ) |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: σ^2 / (2θ) | Statement: [Ornstein–Uhlenbeck process, hasStationaryVariance, σ^2 / (2θ)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationaryVariance Context triple: [Ornstein–Uhlenbeck process, hasStationaryVariance, σ^2 / (2θ)]
-
A.
hasVariance
Indicates that there is a measurable degree of variability or dispersion in the values or outcomes associated with the related entities.
-
B.
hasVarianceSymbol
chosen
Indicates that one entity is associated with, or represented by, a specific variance symbol in a mathematical or statistical context.
-
C.
hasVariability
Indicates that an entity exhibits variation or fluctuation in its state, value, or characteristics over time or across instances.
-
D.
hasCovarianceStructure
Indicates that one entity possesses or is associated with a specific covariance structure that characterizes how its variables co-vary.
-
E.
hasIndependentVariable
Indicates that one entity functions as the independent variable that influences or determines another entity in a relationship or experiment.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7336feff88190b638b7d62d34da0e |
completed | April 9, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69d6f3188f00819094ee8d65b187a333 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:18 p.m.